Earticle

초록

영어

Increasing number of subjective text appears on the internet which contains a lot of information. In this paper, we study how to apply supervised learning techniques to solve sentiment classification problems. Using the Tibetan news as data, we find that standard supervised learning techniques definitively outperform human-produced baselines. Moreover, we find that selecting the words with words with polarity as feature, the special syntactic structure such as exclamation sentence pattern, etc. as feature can improve the performance of sentiment classification. Conclusively, the research of sentiment analysis is a more challenging problem.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. The Supervised Learning Method
  2.2. The Unsupervised Learning Method
 3. Classification Algorithm
  3.1. Naïve Bayes
  3.2. Maximum Entropy
 4. The Method of Generating Tibetan Text Feature
  4.1. The Classification Model
  4.3. The Experimental Data
  4.4. The Experiments
 5. Experimental Results and Analysis
 Acknowledgement
 References

키워드

저자

• Lirong Qiu [ Department of Information Technology, Minzu University of China, Beijing ]
• Zhen Zhang [ Department of Information Technology, Minzu University of China, Beijing ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605